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The distribution of surface water is highly dynamic both spatially and temporally, and very sensitive to climate change and anthropogenic interference, especially in highly urbanized regions. The abundance of surface water and its spatiotemporal distribution indicate the availability of water resources and the difficulty of water development and utilization. Consequently, it is essential to perform reconstruction and trend prediction of surface water body changes with high accuracy in time and space for scientific research as well as for practical purposes such as flood prevention, sustainable development of water resource systems, and regional ecological management. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is highly developed economically and societally and is characterized by a subtropical humid monsoon climate with an extensive distribution of surface water bodies. Under the integrated pressure of continuous natural environmental change and intense human activity, concerns regarding both the water environment and water security in this area is increasing at an unprecedented rate. In this study, we collected water distribution information from 2001 to 2020 at 5-year intervals, as well as annual and monthly water extent dynamics data for the GBA from the Global Land Analysis & Discovery database. Subsequently, the spatial and temporal changes in water extent, frequency, and distribution characteristics in the last two decades were investigated using remote sensing and geographic information system methods. Finally, elastic analysis was conducted to identify the sensitivity of water body changes to changing environmental parameters, including precipitation, temperature, and urbanization. Focusing on the national and regional strategic needs of GBA, the research objectives of this study were to explore the mechanism of temporal and spatial dynamics of the water extent and frequency, trace the volatile areas of regional water changes, clarify the driving factors of water distribution changes, and provide a basis and guidance for comprehensive management of water resources, flood disaster prevention, and control in areas with high human activity. The first result shows that in the last two decades, the water extent and frequency of the GBA are on a decreasing trend, with an average decreasing rate of 50, 31, and 11 km2/a for maximum, annual, and permanent water extent, respectively, while a large proportion of seasonal water bodies changed into permanent and flood-prone bodies during the same period. Second, although the water frequency showed distinct spatial and urban/rural heterogeneity, it was on a decreasing trend in most of the GBA, except in the eastern region. A steeper decline was observed in urban areas, especially the emerging urban regions. Third, the dike-pond system decreased both in water extent and frequency, while the large and medium-sized lakes and reservoirs in the upper catchment increased in extent. Finally, elastic analysis suggests the dominant driving factor of regional water body change is land use change, which contributed more than 80% to the decrease in water extent, and the reservoir increase is mainly due to precipitation change. The results of this research provide scientific guidance for regional water resource guarantee, water environment optimization, and sustainable development promotion. In addition, this research can serve as a decision-making reference for the development of the "One Belt One Road" national strategy and Guangdong-Hong Kong-Macao Greater Bay Area.

The distribution of surface water is highly dynamic both spatially and temporally, and very sensitive to climate change and anthropogenic interference, especially in highly urbanized regions. The abundance of surface water and its spatiotemporal distribution indicate the availability of water resources and the difficulty of water development and utilization. Consequently, it is essential to perform reconstruction and trend prediction of surface water body changes with high accuracy in time and space for scientific research as well as for practical purposes such as flood prevention, sustainable development of water resource systems, and regional ecological management. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is highly developed economically and societally and is characterized by a subtropical humid monsoon climate with an extensive distribution of surface water bodies. Under the integrated pressure of continuous natural environmental change and intense human activity, concerns regarding both the water environment and water security in this area is increasing at an unprecedented rate. In this study, we collected water distribution information from 2001 to 2020 at 5-year intervals, as well as annual and monthly water extent dynamics data for the GBA from the Global Land Analysis & Discovery database. Subsequently, the spatial and temporal changes in water extent, frequency, and distribution characteristics in the last two decades were investigated using remote sensing and geographic information system methods. Finally, elastic analysis was conducted to identify the sensitivity of water body changes to changing environmental parameters, including precipitation, temperature, and urbanization. Focusing on the national and regional strategic needs of GBA, the research objectives of this study were to explore the mechanism of temporal and spatial dynamics of the water extent and frequency, trace the volatile areas of regional water changes, clarify the driving factors of water distribution changes, and provide a basis and guidance for comprehensive management of water resources, flood disaster prevention, and control in areas with high human activity. The first result shows that in the last two decades, the water extent and frequency of the GBA are on a decreasing trend, with an average decreasing rate of 50, 31, and 11 km2/a for maximum, annual, and permanent water extent, respectively, while a large proportion of seasonal water bodies changed into permanent and flood-prone bodies during the same period. Second, although the water frequency showed distinct spatial and urban/rural heterogeneity, it was on a decreasing trend in most of the GBA, except in the eastern region. A steeper decline was observed in urban areas, especially the emerging urban regions. Third, the dike-pond system decreased both in water extent and frequency, while the large and medium-sized lakes and reservoirs in the upper catchment increased in extent. Finally, elastic analysis suggests the dominant driving factor of regional water body change is land use change, which contributed more than 80% to the decrease in water extent, and the reservoir increase is mainly due to precipitation change. The results of this research provide scientific guidance for regional water resource guarantee, water environment optimization, and sustainable development promotion. In addition, this research can serve as a decision-making reference for the development of the "One Belt One Road" national strategy and Guangdong-Hong Kong-Macao Greater Bay Area. [ABSTRACT FROM AUTHOR]

Human action Recognition for unknown views, is a challenging task. We propose a deep view-invariant human action recognition framework, which is a novel integration of two important action cues: motion and shape temporal dynamics (STD). The motion stream encapsulates the motion content of action as RGB Dynamic Images (RGB-DIs), which are generated by Approximate Rank Pooling (ARP) and processed by using fine-tuned InceptionV3 model. The STD stream learns long-term view-invariant shape dynamics of action using a sequence of LSTM and Bi-LSTM learning models. Human Pose Model (HPM) generates view-invariant features of structural similarity index matrix (SSIM) based key depth human pose frames. The final prediction of the action is made on the basis of three types of late fusion techniques i.e. maximum (max), average (avg) and multiply (mul), applied on individual stream scores. To validate the performance of the proposed novel framework, the experiments are performed using both cross-subject and cross-view validation schemes on three publically available benchmarks-NUCLA multi-view dataset, UWA3D-II Activity dataset and NTU RGB-D Activity dataset. Our algorithm outperforms existing state-of-the-arts significantly, which is measured in terms of recognition accuracy, receiver operating characteristic (ROC) curve and area under the curve (AUC). [ABSTRACT FROM AUTHOR]

Abstract: River flow fluctuation has an important influence on riparian vegetation dynamics. A temporally segmented stochastic model focusing on a same‐aged population is developed for the purpose of describing both spatial and temporal dynamics of riparian vegetation. In the model, the growth rate of population, rather than carrying capacity, is modeled as the random variable. This model has explicit physical meaning. The model deduces a process‐based solution. From the solution process, the probability density of spatial distribution can be derived; therefore, the spatial distribution of population abundance can be described. The lifespan of a same‐aged population and the age structure of the species‐specific population can also be studied with the aid of this temporally segmented model. The influence of correlation time of river flow fluctuation is also quantified according to the model. The calibration of model parameters and model application are discussed. The model provides a computer‐aided method to simulate and predict vegetation dynamics during river flow disturbances. Meanwhile, the model is open and allows for more accurate and concrete modeling of growth rate. Because of the Markov property involved in the process‐based solution, the model also has the ability to deal with cases of nonstationary disturbances. [ABSTRACT FROM AUTHOR]

Visitor use to parks and protected areas is very dynamic. Previous studies have used geospatial models to better understand visitor flow. Geospatial data give a more accurate and precise insight to visitor movements, and investigating both space and time together in one analysis provides a more holistic understanding of visitor use. This study uses a toolbox created for ArcGIS that combines space and time into one analysis to identify space-time hot and cold spots. By entering data of stopping behaviors of visitors driving along a narrow, rustic park road, spatial temporal hot and cold spots were classified and then described by associated demographic data that was collected. The results show statistically significant spatial-temporal trends among stopping behaviors of visitors in vehicles. Such information is valuable to park managers to better understand and manage visitor flows through an area. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new spatial temporal predictor using support vector machine (SVM) and data fusion technique. SVMs are used as temporal predictors at different spatial domains and spatial temporal prediction is achieved by prediction fusion. Our proposed prediction fusion technique improves the prediction accuracy even in a non-Gaussian environment. The performance of the proposed spatial temporal predictor is analyzed. Based on real-life radar data, the proposed spatial temporal approach is shown to provide a more accurate model for sea-clutter data than the conventional methods. [ABSTRACT FROM AUTHOR]